import load_data
import numpy as np
import math
import torch
import torch.nn.functional as F


# print(load_data.load_tiled_segment_size('BUPT_gate', 5))
# print(load_data.load_segment_size('BUPT_gate'))
from train import tileSet

# a = np.zeros((6, 8))
# # print(a)
# count = 0
# for i in range(6):
#     for j in range(8):
#         a[i][j] = count
#         count += 1
# # print(a)
#
# a = np.roll(a, -1, axis=1)
# print(a)
# a[1, :4] = [100, 99, 98, 97]
# print(a)

# tile_tmp = tileSet.tile(0, load_data.load_tiled_segment_size('BUPT_gate', 0))
# s_ = [0.6635155301207618, 0.674472151547843, 0.08344303003592259, 55229.0, 242811.0, 643781.0, 4206482.0, 0.1, 0.010439379948423876]
# tile_tmp.quality = 2
# print(tile_tmp.quality)

# print(round(3456.78/1000, 3))
# print(type(round(3456/1000, 3)))

# throughput = 10
# print(throughput)
# throughput = np.repeat(np.expand_dims(throughput, axis=0), 6, axis=0)
# print(throughput)

# a = 123456
# print(a // 100)
# print(a)

# count = 0
#
# while True:
#     for j in range(6):
#         if j == 5:
#             count += 1
#         if count == 10:
#             break
#     else:
#         continue
#     break
#
# print(count)

# print(math.acos(1) / math.pi)
# print(math.acos(0) / math.pi)
# print(math.acos(-1) / math.pi)
# print(math.pi)
# print(math.acos(0))

# print(F.softmax(torch.Tensor([-100, 100]), dim=0))
print(0.5 < 1 ? 'y' : 'n')